基于卷积神经网络的观众笑声预测
Predicting Audience's Laughter Using Convolutional Neural Network
摘要：
为了自动评估演讲者的幽默使用，我们建立了一个基于TED演讲的幽默话语的演示语料库。与以前支持幽默识别研究的数据资源相比，我们有几个优势，包括（a）来自同质数据集的正面和负面实例，（b）包含大量的扬声器，以及（c）是开放的。重点使用词汇线索的幽默识别，我们系统地比较了一个新出现的文本分类方法的基础上卷积神经网络（CNN）与一个行之有效的传统方法，使用语言知识。CNN方法的优点是既能获得更高的检测精度，又能自动学习基本特征。
英文摘要：
For the purpose of automatically evaluating speakers' humor usage, we build a presentation corpus containing humorous utterances based on TED talks. Compared to previous data resources supporting humor recognition research, ours has several advantages, including (a) both positive and negative instances coming from a homogeneous data set, (b) containing a large number of speakers, and (c) being open. Focusing on using lexical cues for humor recognition, we systematically compare a newly emerging text classification method based on Convolutional Neural Networks (CNNs) with a well-established conventional method using linguistic knowledge. The advantages of the CNN method are both getting higher detection accuracies and being able to learn essential features automatically.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
